PT Journal
AU David Castells
   Vinh Ngo
   Juan Borrego-Carazo
   Marc Codina
   Carles Sanchez
   Debora Gil
   Jordi Carrabina
TI A Survey of FPGA-Based Vision Systems for Autonomous Cars
SO IEEE Access
JI ACESS
PY 2022
BP 132525
EP 132563
VL 10
DI 10.1109/ACCESS.2022.3230282
DE Autonomous automobile; Computer vision; field programmable gate arrays; reconfigurable architectures
AB On the road to making self-driving cars a reality, academic and industrial researchers are working hard to continue to increase safety while meeting technical and regulatory constraints Understanding the surrounding environment is a fundamental task in self-driving cars. It requires combining complex computer vision algorithms. Although state-of-the-art algorithms achieve good accuracy, their implementations often require powerful computing platforms with high power consumption. In some cases, the processing speed does not meet real-time constraints. FPGA platforms are often used to implement a category of latency-critical algorithms that demand maximum performance and energy efficiency. Since self-driving car computer vision functions fall into this category, one could expect to see a wide adoption of FPGAs in autonomous cars. In this paper, we survey the computer vision FPGA-based works from the literature targeting automotive applications over the last decade. Based on the survey, we identify the strengths and weaknesses of FPGAs in this domain and future research opportunities and challenges.
ER